Predictive targeting in Australian orogenic-gold systems at the deposit to district scale using numerical modelling

Abstract

3D numerical models of coupled deformation and fluid flow provide a useful tool for exploration in orogenic-gold systems. Numerical modelling of ore-forming processes can lead to a reduction in targeting and detection risk, thus improving the value proposition of mineral exploration. Hydrothermal mineralisation arises from a complex interplay of deformation, fluid flow, conductive and advective heat transport, solute transport and chemical reactions. Coupled simulation of all of these processes represents a significant computational challenge that cannot be solved within the time-scale of a mineral exploration program. However, the problem can be simplified by identifying a subset of processes representing the first-order controls on mineralisation at the scale of interest. For most orogenic-gold systems, it is argued that the first-order controls on mineralisation at the camp to deposit scale are deformation-induced dilation, fluid flow and fluid focusing. Hence, numerical models of coupled deformation and fluid flow can provide a quantitative insight into the localisation of oreforming fluids in this type of system. In two case studies, known deposits were modelled in order to determine the critical deformation and fluid-flow-related factors controlling the localisation of mineralisation in these systems. The quantitative results from the forward models were then used as a basis for constructing predictive models that were applied to regional targeting, prospect ranking and selecting the choice of detection methods. Both case studies show that numerical modelling is capable of reproducing the distribution of known anomalism, and that it can predict anomalies that were not expected or accounted for by purely empirical analysis.

Discovered and undiscovered gold endowment of Turkey: a quantitative mineral resource assessment using GIS and rank statistical analysis

“…However, the probability of success in mineral exploration is generally very low due to the attendant high geological uncertainty. For investors, exploration managers, or government agencies there is no robust method that can improve the chance of technical and financial success in a cost-effective manner in mineral exploration (Potma et al 2008;Kreuzer et al 2008). In general, chances of success in exploration are tiny; available statistics suggest that a success rating of <0.1% is the norm and only in favorable circumstances will this rise above 1% (Moon et al 2006).…”

“…Targeting risk can be managed by maximizing the probability of the existence of an orebody in the target area, which can be discovered by some detection technology (e.g., drilling, geochemistry, geophysics etc. ; Potma et al 2008). A number of geostatistical and statistical techniques have been employed to minimize the technical and financial risks related to target selection, e.g., prospectivity analysis, GISassisted manual, or GIS-driven automated.…”

Coupled Models of Brittle-plastic Deformation and Fluid Flow: Approaches, Methods, and Application to Mesoproterozoic Mineralisation at Mount Isa, Australia

“…Coupled modelling of Mohr-Coulomb plasticity and Darcy flow has proven useful in understanding deformation driven fluid flow at the mine to district scale, where a number of different rock types occur in a particular geological architecture. Schaubs et al (2006) and Potma et al (2008) have shown how this method can be successfully applied as a tool for target generation in mineral exploration. Ideally, in validation stage, the impact of different strain geometries on dilation and fluid flow is tested against known alteration and mineralisation patterns in a data rich location, before being applied for exploration in data poor areas.…”

“…Models In both cases there is a potential to drive flow upward in regions where fluid pressure departs from hydrostatic gradient, but the high pore pressure below the seal (in b) may favour tensile failure of the rock at the top of the reservoir are either purely elastic (Cox and Ruming 2004;Micklethwaite and Cox 2004), based on damage mechanics concepts (Sheldon and Micklethwaite 2007), or simulate plasticity by slip along interfaces of discrete elements representing weak fault systems (Ojala et al 1993;Holyland and Ojala 1997;Mair et al 2000). Other workers have used numerical models based on coupling Mohr-Coulomb plasticity with Darcy flow as implemented in the finite difference codes FLAC (Itasca 2000) and FLAC 3D (Itasca 2003;Cundall and Board 1988), to simulate dilation in complex multi-material geometries (Jiang et al 1997;Gow et al 2002;Ord et al 2002;Ord and Oliver 1997;Schaubs and Zhao 2002;Sorjonen-Ward et al 2002;Gessner et al 2006;McLellan et al 2004;Sheldon and Ord 2005;Robinson et al 2006;Schaubs et al 2006;Vos et al 2007;Zhang et al 2006aZhang et al , b, 2007Potma et al 2008). The latter technique is based on coupling Darcy flow to a Mohr-Coulomb constitutive model including plasticity.…”

“…This article is based on material presented during the short course 'Process modelling of hydrothermal systems' at RWTH International Academy in Aachen, Germany in 2006, and 2008.…”